Early Detection & Stage Classification of Parkinson’s Disease using Deep Learning / (Record no. 607199)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01951nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Zeeshan, Muhammad Muzzamil |
| 245 ## - TITLE STATEMENT | |
| Title | Early Detection & Stage Classification of Parkinson’s Disease using Deep Learning / |
| Statement of responsibility, etc. | Muhammad Muzzamil Zeeshan |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 38p. ; |
| Dimensions | 30cm. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Parkinson’s disease (PD) is caused by a lack of dopamine production by the substantia nigra in<br/>the brain. It is an enduring disorder without any cure, making it a burden on the patient and the<br/>society. PD is a complex disorder marked by many physical and non-physical manifestations,<br/>which differ for everyone. Clinicians might misdiagnose, waste time and resources to get a<br/>patient’s diagnosis or do not have enough expertise to diagnose a patient. Deep learning models<br/>tend to overfit with new data; thus, to prevent variance in the model, merging outputs has been<br/>proven effective. This study proposes an ensemble deep learning model, to automate PD<br/>detection and stage classification, which can handle different data by combining rules. The<br/>ensemble model (TransConvNet) links two state-of-the art deep learning models in decision level<br/>ensembling. The outputs are fused using averaging voting. Both neural networks utilize gait data<br/>provided by Physionet. The validation accuracy for PD detection reached 82%, while for PD<br/>stage classification, it reached 73%. This model delivers competitive and top-notch performance<br/>for severity and detection prediction for PD using gait. This can be used as a tool for PD<br/>detection or monitoring its development. Future work might include addition of models for better<br/>performance and reducing the training time of the models.<br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. KASHIF JAVED |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/39590">http://10.250.8.41:8080/xmlui/handle/123456789/39590</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | 12/06/2023 | 610 | SMME-TH-939 | Thesis |